Machine-learning (ML)-based systems and methods for maximizing resource utilization

ABSTRACT

Systems and methods for maximizing resource utilization in a digital communication system are provided. A method may include receiving a direction for a first one of a plurality of edge devices to execute a task, wherein the edge devices communicate with each other and with a central server via a communication network. The central server may be operated by an entity that is independent of the communication network. The plurality of edge devices may each include an authenticated software application that is provided by the entity. The method may also include calculating, via the ML engine for each of the plurality of edge devices, a predicted resource availability score, and distributing, via the central server, the task among the plurality of edge devices based on the predicted resource availability scores.

FIELD OF TECHNOLOGY

Aspects of the disclosure relate to digital systems. Specifically,aspects of the disclosure relate to machine-learning (ML)-based systemsand methods for maximizing resource utilization in a digital system.

BACKGROUND OF THE DISCLOSURE

Individual computing devices are commonly directed to perform computingtasks. Often, a device will have insufficient resources to execute thecomputing task. Typically, in these scenarios, execution of the task isdelayed or cancelled. Delaying or cancelling a computing task can havedetrimental consequences.

It would be desirable, therefore, to provide systems and methods formaximizing resource utilization in a digital system.

SUMMARY OF THE DISCLOSURE

Aspects of the disclosure relate to relate to a machine-learning(ML)-based digital communication system with maximized resourceutilization. The system may include a central server. The central servermay include a processor and a non-transitory memory storing computerexecutable instructions that, when run on the processor, are configuredto cause the processor to transmit communications over a communicationnetwork. The system may also include a machine-learning (ML) engine.

The system may include a plurality of edge devices. The plurality ofedge devices may be located at a logical edge of the communicationnetwork. The plurality of edge devices may be configured to communicatewith each other and with the central server via the communicationnetwork.

The central server may be operated by an entity that is independent ofthe communication network. The plurality of edge devices may eachinclude an authenticated software application that may be provided bythe entity.

When a first one of the plurality of edge devices is directed to executea task, the ML engine may calculate, for each of the plurality of edgedevices, a predicted resource availability score. The central server maydistribute the task among the plurality of edge devices based on thepredicted resource availability scores.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and advantages of the disclosure will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative system in accordance with principles of thedisclosure;

FIG. 2 shows an illustrative apparatus in accordance with principles ofthe disclosure;

FIG. 3 shows an illustrative diagram in accordance with principles ofthe disclosure;

FIG. 4 shows another illustrative diagram in accordance with principlesof the disclosure; and

FIG. 5 shows an illustrative flowchart in accordance with principles ofthe disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Systems and methods for resource balancing and maximization areprovided. Aspects of the disclosure relate to a machine-learning(ML)-based digital communication system. Methods according to thedisclosure may, in certain embodiments, be performed completely or inpart by the system. The system may include a central server. The centralserver may include a processor and a non-transitory memory storingcomputer executable instructions that, when run on the processor, areconfigured to cause the processor to transmit communications over acommunication network. The central server may be cloud-based. The systemmay also include a machine-learning (ML) engine.

The central server may be operated by an entity that is independent ofthe communication network. For example, the communication network may bea cellular network. The communication network may be an internetnetwork. The internet network may include wired and/or wireless (e.g.,Wi-Fi) components. The entity may be an institution. The entity may be acorporation. The entity may, for example, be a financial institutionwith a large network of employees and customers that operate devicesthat communicate over the communication network.

The system may include edge devices. An edge device may be a computingdevice that may be located at a logical edge of the communicationnetwork. A device at the logical edge of a network may be a device thatis not part of a communication pipeline of the communication network toconduct transmissions from the central server to other networkdevices—rather, the edge device will typically be an endpoint on anetwork map. The edge devices may be configured to communicate with eachother and/or with the central server via the communication network.

The edge devices may each include an authenticated software application(an “app”) that may be provided by the entity. The edge devices in thesystem may thereby achieve a level of trust with each other that exceedsthe level of trust with ordinary devices that are connected via thecommunication network. For example, in the illustrative scenario wherethe entity is a financial institution, each edge device may include anapp that is associated with the institution. The app may be a dedicatedapp in which a user can perform operations (e.g., access to accounts,information, and transactions) relating to the user's association withthe institution. The app may also include a level of security that isenforced across all edge devices. The level of security may includeencryption and/or an authentication protocol. The level of security may,for example, include password protection and/or tokening. The level ofsecurity may thereby contribute to the elevated level of trust achievedbetween the edge devices in the system.

The edge devices may be directed to perform a task. The task may be acomputing task. The task may be requested by the central server. Thetask may, in some scenarios, be requested by a user of the edge device.The user may request the task via the software app.

When a first one of the edge devices is directed to execute a task, theML engine may calculate, for each of the edge devices, a predictedresource availability score. The predicted resource availability scorefor a device may represent a calculated prediction of the ease withwhich the device may be able to execute the task. For example, the scoremay be a number between 0 and 1, representing an increasing scale ofability to execute the task. A threshold number, for example 0.5, mayrepresent a passing score (i.e., that the task can be successfullyexecuted). Increased efficiency factors may cause the score to increase.For example, the predicted ability of the device to execute the taskwith increased speed while utilizing less resources (e.g., bandwidth,processing power, battery power, etc.) may result in a higher score thana device that is predicted to execute the task in more time whileutilizing more, or a higher percentage, of available resources. Thepredicted resource availability score may also take into account futuretasks that are scheduled to be performed on a device. For example, whena future task is scheduled on a given device for the time period duringwhich the current task is supposed to be performed, that may be a factorthat reduces the score for the given device.

The task may be distributed among the edge devices based on thepredicted resource availability scores. For example, the task may bereassigned from the original edge device to the one of the other edgedevices that achieved the highest the predicted resource availabilityscore. The task may also be split and distributed to multiple edgedevices. The multiple edge devices may include the device to which thetask was originally assigned. The reassignment and distribution may beperformed by the central server. In some embodiments, the reassignmentand/or distribution may be performed by one or more edge devices. Forexample, the device to which the task was originally assigned may itselfreassign the task and may transmit to another device the request toperform the task.

In some embodiments, the task may include storing sensitive information.The central server may segment the sensitive information into aplurality of non-sensitive segments and may store the non-sensitivesegments separately on the edge devices. Distribution of tasks ingeneral, and in particular tasks that include sensitive information, maybe plausible in part due to the elevated level of trust achieved amongthe edge devices in the system, which may have been achieved, at leastin part, due to the level of security enforced by the entity app on eachof the edge devices.

In certain embodiments, the task may utilize processing power, batterypower, memory, and/or connectivity. Connectivity may include qualityand/or quantity of connectivity to the communication network. Forexample, higher connectivity may include a stronger connection and/orbroader bandwidth with which a device is able to communicate via thecommunication network.

The first edge device may have insufficient processing power, batterypower, memory, and connectivity to execute the task. The ML engine maycalculate that one or more of the edge devices have sufficientprocessing power, battery power, memory, and connectivity to execute thetask. The central server may distribute the task to the one or more ofthe edge devices.

In some embodiments, the ML engine may use a plurality of factors incalculating the predicted resource availability score. The factors mayinclude inherent resources of each edge device, such as processingpower, battery power, memory, and connectivity. The factors may alsoinclude historical resource usage data for each edge device. The factorsmay also include data specific to the task, which may include time andresources necessary for the task and/or historical data relating toresource usage for this or similar tasks.

In certain embodiments, the plurality of edge devices may include afirst set of computing devices and/or a second set of computing devices.The first set of computing devices may be owned and operated by theentity. The first set of devices may include computing devices andservers installed at a physical location of the entity, automated tellermachines (ATMs), and point of sale (POS) terminals.

The second set of computing devices may be owned and operated byindividuals. The second set of computing devices may include primarydevices including desktops, laptops, smart phones, smart watches, andtablets on which the authenticated software application is downloadedand running. The second set of computing devices may also includesecondary devices including internet-of-things (IoT) devices that arecontrolled by the primary devices. In some embodiments, the second setof computing devices may need to receive an opt-in from the individualsto allow the central server to distribute tasks.

In certain embodiments, each of the edge devices may be secured andencrypted.

In some embodiments, when the first edge device has insufficientconnectivity over the communication network and the central server has atransmission to transmit to the first edge device, the central servermay transmit the transmission to a second one of the edge devices thathas sufficient connectivity. The second edge device may thereafterretransmit the transmission to the first edge device via a communicationchannel that is independent of the communication network. Thecommunication channel may for example, be Bluetooth, LoRa WAN, a localarea network (LAN), Visible Light Communication (VLC), Li-Fi, and/or anyother suitable communication channel that may be independent of thecommunication network.

In certain embodiments, the transmission may be a software update. Thesoftware update may, for example, be a patch or other suitable updatethat the central server may be attempting to transmit to some or all ofthe edge devices.

In some embodiments, the task may be a first task and one or more of theedge devices may be assigned one or more other tasks. The central servermay be configured to redistribute the other tasks to free up resourcesfor the first task.

Certain embodiments of the system may also include identifying data thatis not frequently used and moving it to secondary storage (e.g., using aleast used first out protocol). The secondary storage may include thememory of an edge device aside that is different than the device thatwas initially assigned the task.

In some embodiments, if the secondary storage is in a remote location,the data may be split up in multiple small fragments and distributed tothe local nodes along with the address of the destination node creatinga relay to the final destination. Multiple copies of the same datafragments may be distributed to several different edge nodes to ensurethat if one of the node fails complete data can still be recreatedthrough other nodes sending the same data fragment. This could alsoimprove speed of transfer since each node is transmitting only a portionof data and some nodes might be able to transmit the data faster.Therefore, the destination node may receive data fragments from the mostefficient nodes first and use that to recreate the data, and the rest ofthe duplicate data fragments arriving later may be discarded.

In certain embodiments, data may be distributed across two or more edgenodes. Each node may maintain a partial copy of the data and a pointerto the remaining data set. The relevant chunk of data can be retrievedfrom each node and combined during processing at run time withoutbringing all of the data together in a single node.

In some embodiments, the system may allocate a small portion of computeand storage resources of some or all entity devices for edge-to-edgecommunication. This may create a massive network of edge nodes withpotentially unlimited compute and storage resources (e.g., ATM, Phones,Laptops, Routers, Smart TVs, etc.) that can transfer data fragments fromsystem to system until it reaches its final destination. The allocatedstorage in each device may be encrypted so that it cannot be accessed byany user to enhance security and avoid a potential data breach.

In certain embodiments, instead of processing data in central servers,the system may use each node in the compute and storage grid to performindependent data processing and/or validation on their data fragment andrelay the results back to the sender node either directly or throughedge-to-edge communication.

In some embodiments, the system may programmatically identifyunderutilized high performance edge nodes (e.g., an ATM at night) to bea primary node and low power devices as data relay nodes to improve thespeed and efficiency of the edge network. This may, for example, ensurethat a laptop with 20% battery is not used to compute intensive dataprocessing and can instead be used to relay the data. The heavy dataprocessing may instead be done by primary nodes. Roles may be reversedduring other times. For example, a plugged-in laptop during lunch breakmay be used as a primary node and an ATM during peak usage may be usedas a relay node.

In certain embodiments, the system may include decentralizedauthentication using edge nodes. The system may create a securityframework on each node and tokenize authentication. The securityframework may allow a user to be authorized from any of the nodes toinitiate a transaction. For example, when a transaction is authorized byone of the nodes the access may be tokenized and transmitted from nodeto node along with the data. The tokens may be checked by each node inthe network to validate that the transaction was authenticated byprevious nodes before processing the transaction and relaying it to thenew node.

The framework may, in certain embodiments, have multiple types of tiersof security. The tiers may be based on the type of transaction involvedand may be used to relay or mask specific type of information requestedby the edge node depending on their level of security clearance. Forexample—a public facing edge node may not be part of a confidential datatransmission, so all confidential information may be stripped by thesending node before transmission.

Apparatus and methods described herein are illustrative. Apparatus andmethods in accordance with this disclosure will now be described inconnection with the figures, which form a part hereof. The figures showillustrative features of apparatus and method steps in accordance withthe principles of this disclosure. It is understood that otherembodiments may be utilized, and that structural, functional, andprocedural modifications may be made without departing from the scopeand spirit of the present disclosure.

FIG. 1 shows an illustrative block diagram of system 100 that includescomputer 101. Computer 101 may alternatively be referred to herein as a“server” or a “computing device.” Computer 101 may be a workstation,desktop, laptop, tablet, smart phone, or any other suitable computingdevice. Elements of system 100, including computer 101, may be used toimplement various aspects of the systems and methods disclosed herein.

Computer 101 may have a processor 103 for controlling the operation ofthe device and its associated components, and may include RAM 105, ROM107, input/output module 109, and a memory 115. The processor 103 mayalso execute all software running on the computer—e.g., the operatingsystem and/or voice recognition software. Other components commonly usedfor computers, such as EEPROM or Flash memory or any other suitablecomponents, may also be part of the computer 101.

The memory 115 may be comprised of any suitable permanent storagetechnology—e.g., a hard drive. The memory 115 may store softwareincluding the operating system 117 and application(s) 119 along with anydata 111 needed for the operation of the system 100. Memory 115 may alsostore videos, text, and/or audio assistance files. The videos, text,and/or audio assistance files may also be stored in cache memory, or anyother suitable memory. Alternatively, some or all of computer executableinstructions (alternatively referred to as “code”) may be embodied inhardware or firmware (not shown). The computer 101 may execute theinstructions embodied by the software to perform various functions.

Input/output (“I/O”) module may include connectivity to a microphone,keyboard, touch screen, mouse, and/or stylus through which a user ofcomputer 101 may provide input. The input may include input relating tocursor movement. The input may relate to transmissions and executions oftasks in a digital communication network. The input/output module mayalso include one or more speakers for providing audio output and a videodisplay device for providing textual, audio, audiovisual, and/orgraphical output. The input and output may be related to computerapplication functionality. The input and output may be related totransmissions and executions of tasks in a digital communicationnetwork.

System 100 may be connected to other systems via a local area network(LAN) interface 113.

System 100 may operate in a networked environment supporting connectionsto one or more remote computers, such as terminals 141 and 151.Terminals 141 and 151 may be personal computers or servers that includemany or all of the elements described above relative to system 100. Thenetwork connections depicted in FIG. 1 include a local area network(LAN) 125 and a wide area network (WAN) 129, but may also include othernetworks. When used in a LAN networking environment, computer 101 isconnected to LAN 125 through a LAN interface or adapter 113. When usedin a WAN networking environment, computer 101 may include a modem 127 orother means for establishing communications over WAN 129, such asInternet 131.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween computers may be used. The existence of various well-knownprotocols such as TCP/IP, Ethernet, FTP, HTTP and the like is presumed,and the system can be operated in a client-server configuration topermit a user to retrieve web pages from a web-based server. Theweb-based server may transmit data to any other suitable computersystem. The web-based server may also send computer-readableinstructions, together with the data, to any suitable computer system.The computer-readable instructions may be to store the data in cachememory, the hard drive, secondary memory, or any other suitable memory.

Additionally, application program(s) 119, which may be used by computer101, may include computer executable instructions for invoking userfunctionality related to communication, such as e-mail, Short MessageService (SMS), and voice input and speech recognition applications.Application program(s) 119 (which may be alternatively referred toherein as “plugins,” “applications,” or “apps”) may include computerexecutable instructions for invoking user functionality relatedperforming various tasks. The various tasks may be related totransmissions and executions of tasks in a digital communicationnetwork.

Computer 101 and/or terminals 141 and 151 may also be devices includingvarious other components, such as a battery, speaker, and/or antennas(not shown).

Terminal 151 and/or terminal 141 may be portable devices such as alaptop, cell phone, Blackberry™, tablet, smartphone, or any othersuitable device for receiving, storing, transmitting and/or displayingrelevant information. Terminals 151 and/or terminal 141 may be otherdevices. These devices may be identical to system 100 or different. Thedifferences may be related to hardware components and/or softwarecomponents.

Any information described above in connection with database 111, and anyother suitable information, may be stored in memory 115. One or more ofapplications 119 may include one or more algorithms that may be used toimplement features of the disclosure, and/or any other suitable tasks.

The invention may be operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, tablets, mobile phones, smart phones and/or otherpersonal digital assistants (“PDAs”), multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc., that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

FIG. 2 shows illustrative apparatus 200 that may be configured inaccordance with the principles of the disclosure. Apparatus 200 may be acomputing machine. Apparatus 200 may include one or more features of theapparatus shown in FIG. 1 . Apparatus 200 may include chip module 202,which may include one or more integrated circuits, and which may includelogic configured to perform any other suitable logical operations.

Apparatus 200 may include one or more of the following components: I/Ocircuitry 204, which may include a transmitter device and a receiverdevice and may interface with fiber optic cable, coaxial cable,telephone lines, wireless devices, PHY layer hardware, a keypad/displaycontrol device or any other suitable media or devices; peripheraldevices 206, which may include counter timers, real-time timers,power-on reset generators or any other suitable peripheral devices;logical processing device 208, which may compute data structuralinformation and structural parameters of the data; and machine-readablememory 210.

Machine-readable memory 210 may be configured to store inmachine-readable data structures: machine executable instructions (whichmay be alternatively referred to herein as “computer instructions” or“computer code”), applications, signals, and/or any other suitableinformation or data structures.

Components 202, 204, 206, 208 and 210 may be coupled together by asystem bus or other interconnections 212 and may be present on one ormore circuit boards such as 220. In some embodiments, the components maybe integrated into a single chip. The chip may be silicon-based.

FIG. 3 shows diagram 300 in accordance with aspects of the disclosure.Diagram 300 shows a system architecture that may include central server301. Central server 301 may include processor 303, memory 305, and MLengine 307. The system may also include edge devices 311-319, which may,for example, include a laptop, a smartphone, a smartwatch, and branchinfrastructure, respectively. Edge devices 311-319 may be configured tocommunicate with central server 301 via primary communication network309. When one of the devices, e.g., edge device 311, is directed toexecute a computing task, the task may be redistributed to some or allof the edge devices based on predicted resource availability scorescalculated by ML engine 307. Redistributing the task may includererouting a transmission, over secondary communication channel 319, whena device to which the transmission is intended is unable to receive thetransmission via primary network 309.

FIG. 4 shows diagram 400 in accordance with aspects of the disclosure.Diagram 4 shows one example of redistribution of a computing task.Device 403 may be directed to perform a task that may include storingsensitive data 401. Sensitive data 401 may include multiple portions,A-D. Sensitive data 403 may be vulnerable if stored in its entirety ondevice 403. The system may therefore segment sensitive data 403 intosegments 405-411, each off which may include one portion of sensitivedata 403. Each of segments 406-411 may be stored separately on edgedevices 413-419. Device 413 may, in some embodiments, be original device403. Devices 413-419 may correspond to devices 311-317 shown in FIG. 3 .

FIG. 5 shows flowchart 500 in accordance with aspects of the disclosure.Step 501 of flowchart 500 includes downloading an app associated with anentity on an edge device. Step 503 includes the device communicatingwith a central server over a primary communication network. Step 505includes the device being directed to execute a computing task. Step 507includes the device receiving an opt-in to participate in loadbalancing. Step 509 includes a ML engine calculating recourseavailability scores for each edge device in the system. At step 511, ifthe edge device is unavailable via the primary communication network,the system may, at step 513, reroute a transmission via a second edgedevice over a secondary communication network. At step 515, if the edgedevice has insufficient resources to execute the task, the system may,at step 517, redistribute the task to other edge devices. Theredistribution may be based on the resource availability scores. Theredistribution may be performed over the secondary communicationnetwork. At step 519, if the task includes storage of sensitive data,the system may, at step 521, segment the sensitive data and distributethe segmented data to other edge devices.

The steps of methods may be performed in an order other than the ordershown and/or described herein. Embodiments may omit steps shown and/ordescribed in connection with illustrative methods. Embodiments mayinclude steps that are neither shown nor described in connection withillustrative methods.

Illustrative method steps may be combined. For example, an illustrativemethod may include steps shown in connection with another illustrativemethod.

Apparatus may omit features shown and/or described in connection withillustrative apparatus. Embodiments may include features that areneither shown nor described in connection with the illustrativeapparatus. Features of illustrative apparatus may be combined. Forexample, an illustrative embodiment may include features shown inconnection with another illustrative embodiment.

The drawings show illustrative features of apparatus and methods inaccordance with the principles of the invention. The features areillustrated in the context of selected embodiments. It will beunderstood that features shown in connection with one of the embodimentsmay be practiced in accordance with the principles of the inventionalong with features shown in connection with another of the embodiments.

One of ordinary skill in the art will appreciate that the steps shownand described herein may be performed in other than the recited orderand that one or more steps illustrated may be optional. The methods ofthe above-referenced embodiments may involve the use of any suitableelements, steps, computer-executable instructions, or computer-readabledata structures. In this regard, other embodiments are disclosed hereinas well that can be partially or wholly implemented on acomputer-readable medium, for example, by storing computer-executableinstructions or modules or by utilizing computer-readable datastructures.

Thus, methods and systems for maximizing resource utilization in digitalcommunications systems are provided. Persons skilled in the art willappreciate that the present invention can be practiced by other than thedescribed embodiments, which are presented for purposes of illustrationrather than of limitation, and that the present invention is limitedonly by the claims that follow.

What is claimed is:
 1. A machine-learning (ML)-based digitalcommunication system with maximized resource utilization, the systemcomprising: a central server comprising a processor and a non-transitorymemory storing computer executable instructions that, when run on theprocessor, are configured to cause the processor to transmitcommunications over a communication network; a plurality of edgedevices, wherein the plurality of edge devices are located at a logicaledge of the communication network and communicate with each other andwith the central server via the communication network; and amachine-learning (ML) engine; wherein: the central server is operated byan entity that is independent of the communication network; theplurality of edge devices each comprise an authenticated softwareapplication that is provided by the entity; and when a first one of theplurality of edge devices is directed to execute a task: the ML enginecalculates, for each of the plurality of edge devices, a predictedresource availability score, wherein the predicted resource availabilityscore is reduced based on a future task scheduled to be performed on adevice; and the central server distributes the task among the pluralityof edge devices based on the predicted resource availability score. 2.The system of claim 1 wherein the task comprises storing sensitiveinformation, and the central server segments the sensitive informationinto a plurality of non-sensitive segments and stores the non-sensitivesegments separately on the plurality of edge devices.
 3. The system ofclaim 1 wherein: the task utilizes processing power, battery power,memory, and connectivity; and when the first edge device hasinsufficient processing power, battery power, memory, and connectivityto execute the task, the central server distributes the task to one ormore of the plurality of edge devices, wherein the ML engine calculatesthat the one or more of the plurality of edge devices have sufficientprocessing power, battery power, memory, and connectivity to execute thetask.
 4. The system of claim 1 wherein the ML engine uses a plurality offactors in calculating the predicted resource availability score, andthe plurality of factors comprise: inherent resources of each edgedevice, said inherent resources comprising processing power, batterypower, memory, and connectivity; and historical resource usage data foreach edge device.
 5. The system of claim 1 wherein the plurality of edgedevices comprise: a first set of computing devices that are owned andoperated by the entity, said first set of devices comprising computingdevices and servers installed at a physical location of the entity,automated teller machines (ATMs), and point of sale (POS) terminals; anda second set of computing devices that are owned and operated byindividuals, said second set of computing devices comprising: primarydevices comprising desktops, laptops, smart phones, smart watches, andtablets on which the authenticated software application is downloadedand running; and secondary devices comprising internet of things (IoT)devices that are controlled by the primary devices.
 6. The system ofclaim 5 wherein the second set of computing devices received an opt-infrom the individuals to allow the central server to distribute tasks. 7.The system of claim 1 wherein each of the plurality of edge devices issecured and encrypted.
 8. The system of claim 1 wherein, when the firstedge device has insufficient connectivity over the communication networkand the central server has a transmission to transmit to the first edgedevice, the central server transmits the transmission to a second one ofthe edge devices that has sufficient connectivity, and the second edgedevice transmits the transmission to the first edge device via acommunication channel that is independent of the communication network.9. The system of claim 8 wherein the transmission is a software update.10. The system of claim 1 wherein: the task is a first task and one ormore of the plurality of edge devices is assigned one or more othertasks; and the central server is configured to redistribute the othertasks to free up resources for the first task.
 11. A machine-learning(ML)-based method for maximizing resource utilization in a digitalcommunication system, the method comprising: receiving a direction for afirst one of a plurality of edge devices to execute a task, wherein: theplurality of edge devices are located at a logical edge of acommunication network; the plurality of edge devices communicate witheach other and with a central server via the communication network; thecentral server is operated by an entity that is independent of thecommunication network; and the plurality of edge devices each comprisean authenticated software application that is provided by the entity;calculating, via the ML engine for each of the plurality of edgedevices, a predicted resource availability score, wherein the predictedresource availability score is reduced based on a future task scheduledto be performed on a device; and distributing, via the central server,the task among the plurality of edge devices based on the predictedresource availability score.
 12. The method of claim 11 wherein the taskcomprises storing sensitive information, and the method furthercomprises: segmenting the sensitive information into a plurality ofnon-sensitive segments; and storing the non-sensitive segmentsseparately on the plurality of edge devices.
 13. The method of claim 11wherein: the task utilizes processing power, battery power, memory, andconnectivity; and when the first edge device has insufficient processingpower, battery power, memory, and connectivity to execute the task, themethod further comprises: determining, via the ML engine, that one ormore of the plurality of edge devices have sufficient processing power,battery power, memory, and connectivity to execute the task; anddistributing the task to the one or more of the plurality of edgedevices.
 14. The method of claim 11 wherein the ML engine uses aplurality of factors in calculating the predicted resource availabilityscore, and the plurality of factors comprise: inherent resources of eachedge device, said inherent resources comprising processing power,battery power, memory, and connectivity; and historical resource usagedata for each edge device.
 15. The method of claim 11 wherein theplurality of edge devices comprise: a first set of computing devicesthat are owned and operated by the entity, said first set of devicescomprising computing devices and servers installed at a physicallocation of the entity, automated teller machines (ATMs), and point ofsale (POS) terminals; and a second set of computing devices that areowned and operated by individuals, said second set of computing devicescomprising: primary devices comprising desktops, laptops, smart phones,smart watches, and tablets on which the authenticated softwareapplication is downloaded and running; and secondary devices comprisinginternet of things (IoT) devices that are controlled by the primarydevices.
 16. The method of claim 15 wherein the second set of computingdevices received an opt-in from the individuals to allow the centralserver to distribute tasks.
 17. The method of claim 11 wherein each ofthe plurality of edge devices is secured and encrypted.
 18. The methodof claim 11 wherein, when the first edge device has insufficientconnectivity over the communication network and the central server has atransmission to transmit to the first edge device, the method furthercomprises: transmitting the transmission from the central server to asecond one of the edge devices that has sufficient connectivity; andretransmitting the transmission from the second edge device to the firstedge device via a communication channel that is independent of thecommunication network.
 19. The method of claim 18 wherein thetransmission is a software update.
 20. The method of claim 11 wherein:the task is a first task and one or more of the plurality of edgedevices is assigned one or more other tasks; and the central server isconfigured to redistribute the other tasks to free up resources for thefirst task.